Associations of Combined Exposure to Metabolic and Inflammatory Indicators with Thyroid Nodules in Adults: A Nested Case-Control Study

Objective To explore associations of combined exposure to metabolic/inflammatory indicators with thyroid nodules. Methods We reviewed personal data for health screenings from 2020 to 2021. A propensity score matching method was used to match 931 adults recently diagnosed with thyroid nodules in a 1 : 4 ratio based on age and gender. Conditional logistic regression and Bayesian kernel machine regression (BKMR) were used to explore the associations of single metabolic/inflammatory indicators and the mixture with thyroid nodules, respectively. Results In the adjusted models, five indicators (ORQ4 vs. Q1: 1.30, 95% CI: 1.07–1.58 for fasting blood glucose; ORQ4 vs. Q1: 1.30, 95% CI: 1.08–1.57 for systolic blood pressure; ORQ4 vs. Q1: 1.26, 95% CI: 1.04–1.53 for diastolic blood pressure; ORQ4 vs. Q1: 1.23, 95% CI: 1.02–1.48 for white blood cell; ORQ4 vs. Q1: 1.28, 95% CI: 1.07–1.55 for neutrophil) were positively associated with the risk of thyroid nodules, while high-density lipoproteins (ORQ3 vs. Q1: 0.75, 95% CI: 0.61–0.91) were negatively associated with the risk of thyroid nodules. Univariate exposure-response functions from BKMR models showed similar results. Moreover, the metabolic and inflammatory mixture exhibited a significant positive association with thyroid nodules in a dose-response pattern, with systolic blood pressure being the greatest contributor within the mixture (conditional posterior inclusion probability of 0.82). No interaction effects were found among the five indicators. These associations were more prominent in males, participants with higher age (≥40 years old), and individuals with abnormal body mass index status. Conclusions Levels of the metabolic and inflammatory mixture have a linear dose-response relationship with the risk of developing thyroid nodules, with systolic blood pressure levels being the most important contributor.


Introduction
Tyroid nodules are overgrown masses of normal thyroid cells in the gland [1], which are classifed into several types: single, multiple, solid, or cystic [2].A previous study reported that the global prevalence of thyroid nodules has reached 4-7%, of which 8-16% turn into thyroid cancer [3].In recent years, the prevalence of thyroid nodules in China has shown a concerning increase, with various studies reporting prevalence rates ranging from 10% to 50% [4][5][6][7].For example, a study involving 6,985,956 participants (mean age: 42.1 ± 13.1 years) from 30 provinces and regions in China indicated an overall prevalence of thyroid nodules of 36.9% [4].Although the rate of thyroid nodules evolving into thyroid cancer in China was similar to the fndings of Burman and Wartofsky [3,8], the high prevalence of thyroid nodules has made thyroid cancer the seventh most prevalent malignant tumor in China [9].In addition, thyroid nodules may cause a variety of clinical sequelae such as thyroid dysfunction, dysphagia, and shortness of breath [10].Tese fndings suggest that thyroid nodules are a concern.
Clinical treatment of thyroid nodules is currently controversial [11]; thus, the prevention of thyroid nodules may be an efective strategy.For example, China launched a mandatory universal salt iodization program in 1996, which has been efective in controlling thyroid-related diseases [12].However, recent studies also suggested that excessive iodine intake may increase the risk of thyroid nodules [12][13][14].Tus, there is an urgent need to identify several additional modifable risk factors, especially key factors, in adults.
Tere were some limitations in previous studies.Firstly, most previous studies only focused on the single efects of metabolic/infammation indicators on thyroid nodules [21,22,27].To our knowledge, no study has explored the combined efects of multiple indicators.But humans often are exposed to multiple indicators simultaneously, which may have interaction efects on health [32].Moreover, the available data are more based on cross-sectional studies and/ or limited sample sizes [33].Tus, we employed a retrospective nested case-control study utilizing a relatively large sample to explore the relationships of combined exposure to metabolic and infammatory indicators with thyroid nodules in this study.Combining previous articles and our database, we selected eight metabolic indicators and six infammatory indicators that may be associated with thyroid nodules [21][22][23][24][25][26][27].Our study aims to (1) assess the associations of the combined exposure to eight metabolic indicators and six infammatory indicators with the occurrence of thyroid nodules, (2) explore the indicators that may have the greatest impact on thyroid nodules in the mixture, (3) investigate interactions among mixture components, and (4) discover the susceptible subgroups.

Study Population.
Te data reported in this study came from the Health Examination Center of Zhongda Hospital afliated with Southeast University, which was performed in Nanjing City, Jiangsu Province, China, from January 1, 2020, to December 31, 2021.Clients with severe diseases or clinical symptoms were led to the emergency or outpatient department.Tus, participants in this study were considered healthy or only have mild illnesses for physical examination.Inclusion criteria were (1) participants with health examination records in 2020 and 2021; (2) years ≥18; (3) there was no abnormal change in thyroid ultrasound in 2020; and (4) comprehensive examination of metabolic/infammatory indicators in 2020.Exclusion criteria were (1) a history of thyroid surgery; (2) suspected Graves' disease or thyroid cancer; and (3) data missing on metabolic/infammatory indicators in 2020.According to the inclusion and exclusion criteria, 931 adults recently diagnosed with thyroid nodules and 3724 controls (1 : 4 matched by age and gender using propensity score matching) were included in the fnal analysis (Figure S1).Te principles of the Helsinki Declaration were followed.All data involving medical records were not publicly available, and no participants have been contacted.Ethical approval was obtained from the ethics committee of the Clinical Research Ethics Committee of Zhongda Hospital Afliated with Southeast University (No.: 2022ZDSYLL218-P01).

Demographic and Anthropometric/Indicator Assessment.
Demographic information was collected for the year of 2020, including gender, age, body mass index (BMI), smoking, drinking, diabetes, and hypertension.Anthropometric measurements were performed by a professionally trained nurse.Participants included in the study were required to take of their shoes, and then their height and weight were measured.In addition, participants were required to rest for at least 5-10 minutes before blood pressure measurement.
Participants were asked to remain fasted from 10:00 pm the previous night and have their blood drawn by a nurse the next morning (8: 00-9: 30).A biochemical automatic analyzer (Dimension RxL Max, Siemens Corporation, German) was used to detect metabolic parameters.Te whole blood count was detected by a whole blood automatic analyzer (BC-6800Plus, Mindray Medical, China).

Defnition of Tyroid Nodules.
Tyroid ultrasonography is performed by experienced sonographers using a highfrequency probe.Tyroid nodules have been defned as any solid (including solid with cystic component) and nodular lesion, which are diferent from the adjacent parenchyma in the thyroid gland by ultrasonography [15].Laboratory technicians were trained by technical support staf to use the machines for analysis and to calibrate the analyzers according to standard quality assurance protocols.
2.6.Statistical Analysis.Te characteristics and metabolic/ infammatory indicators across two groups (thyroid nodules and nonthyroid nodules) were compared using the chisquare test for categorical variables and t-tests for continuous variables.Spearman's correlation coefcient was used to assess the correlations between the indicators measured at the baseline.In addition, intraclass correlation coefcients (ICCs) were used to explore the correlations between indicators measured at 2020 and 2021.In the following regression models, we used a zero-mean normalization approach to standardize metabolic and infammatory indicators.Te histograms showed that most of the indicators after zero-mean normalization were normally or approximately normally distributed (Figure S2).Multivariate conditional logistic regression models were employed to evaluate associations between multiple indicators and thyroid nodules.Participants were categorized into quartiles based on the level of each indicator, and the lowest quartile was used as a reference.Adjusted odds ratio (OR) and 95% confdence interval (CI) were calculated for the occurrence of thyroid nodules.Te covariates included age, gender, diabetes, and hypertension.Te model was adjusted for matching variables to account for residual confounding.False discovery rate (FDR) corrections were used to adjust p values.Since there were many indicators in this study, we screened fve indicators (HDL, FBG, SBP, WBC, and N) based on the above regression results and correlation coefcients (Figure S3) and then included them in the subsequent BKMR models.Given the nonlinear and interactive efects, BKMR analysis was performed to assess the combined efects of multiple metabolic and infammatory indicators [32].Te models were executed up to 10,000 iterations using a Markov chain Monte Carlo algorithm [34].Five indicators were classifed into two groups based on metabolic and infammatory indicators.We selected the key indicators for thyroid nodules by calculating the group posterior inclusion probability (groupPIP) and conditional posterior inclusion probability (condPIP), where the threshold value of PIP was 0.5 [35].Te results of BKMR analysis were as follows: (1) nonlinear and/or nonadditive associations of individual indicators with the risk of thyroid nodules, (2) joint efects of the indicator mixture on the risk of thyroid nodules, (3) the relative importance of individual indicators within the mixture, and (4) interactive efects among mixture components.
Stratifed analyses according to gender (male, female), age (<40, ≥40 years), and BMI (18.5-23.9,<18.5/>23.9kg/ m 2 ) were conducted.Furthermore, we conducted three sensitivity analyses to evaluate the stability of results.First, given the possible confounding and mediating efects of BMI in these associations, BMI was not adjusted in the formal analysis.In the sensitivity analysis, we examined the potential confounding efect of BMI by adding the BMI variable to the BKMR model.Although smoking and drinking are risk factors for thyroid nodules, there is a large amount of missing data for these two factors in this study.We grouped the missing values of smoking or drinking into a category.In the second sensitivity analysis, we examined the potential confounding efect of two factors (dummy variables) by adding them to the BKMR model.Finally, all metabolic and infammatory indicators were included in the BKMR model, and the covariates were controlled for age, gender, diabetes, and hypertension.SPSS (version 20; IBM SPSS Statistics) and R (version 4.0.2;R Foundation for Statistical Computing) were used to conduct statistical analysis.Two-sided P values below 0.05 were considered statistically signifcant.

Characteristics of the Study Population.
Te characteristics of the participants are presented in Table 1.Of all participants, 61.2% were female and 57.3% were aged 40 years and above.A total of 931 individuals were newly diagnosed with thyroid nodules during the study period.Te median maximum diameter of thyroid nodules was 0.30 (interquartile range: 0.24-0.40)mm (Figure S4).Comparisons of the risk of thyroid nodules across groups are also shown in Table 1.Participants with thyroid nodules were more likely to have higher BMI, diabetes, and hypertension than those with nonthyroid nodules.In addition, participants with thyroid nodules had higher levels of LDL, FBG, SBP, DBP, WBC, and N, whereas no signifcant diferences were found for other indicators (Table 1).
Spearman's correlation coefcients between metabolic/ infammatory indicators are displayed in Table S1.Spearman's correlation coefcients ranged from −0.010 to 0.890, with the highest correlation coefcient between TC and LDL (r � 0.888), and the remaining ones in descending order were SBP and DBP (r � 0.787) and WBC and M (r � 0.663).In addition, Table S2 showed that the correlations between all indicators for 2020 and 2021 were moderate to strong (ICCs: 0.520-0.839).S3 and Table 2.After adjusting for age, gender, diabetes, and hypertension, the models showed that fve indicators (FBG, SBP, DBP, WBC, and N) had signifcant positive associations with thyroid nodules (P value <0.05), but HDL had a signifcant negative association (P value <0.05).For example, compared with participants in the lowest quartile of FBG, SBP, DBP, WBC, and N, participants in the highest quartile showed 30% (95% CI: 1.07, 1.58), 30% (95% CI: 1.08, 1.57), 26% (95% CI: 1.04, 1.53), 23% (95% CI: 1.02, 1.48), and 28% (95% CI: 1.07, 1.55) increased the risk of thyroid nodules, respectively.Moreover, compared with participants in the lowest quartile of International Journal of Endocrinology HDL, participants in the 3 rd quartile showed a 25% (95% CI: 0.61, 0.91) decreased the risk of thyroid nodules.After FDR adjustments were made, similar results of statistically signifcant were found.Since there were many indicators in this study, we screened fve indicators (HDL, FBG, SBP, WBC, and N) based on the above regression results and correlation coefcients (Figure S3) and then included them in the subsequent BKMR models.

BKMR Analyses. Figure 1 also showed linear relationships between exposure to single indicators and thyroid
nodules when other indicators' exposure was fxed at the median.Figure 2 showed that a signifcant joint efect of the fve indicators was found when all indicators were at or above their 55th percentile compared to the median.In addition, a slightly decreased and positive association between SBP and thyroid nodules was found when the other four indicators

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International Journal of Endocrinology were fxed at diferent percentiles (25th, 50th, or 75th) (Figure 3).SBP exhibited strong linear associations, which was supported by PIPs in Table 3.One groupPIPs were higher than 0.5, and the condPIP of SBP (0.82) was the highest in the group.Finally, we estimated bivariate exposure-response functions for the fve indicators (Figure 4).We did not fnd a signifcant interaction efect among the fve indicators.S10).Besides, after controlling for BMI (continuous), smoking and drinking, or all 14 indicators, three sensitivity analyses did not materially change our fndings (Figures S11-S13).

Discussion
4.1.Key Findings.In the adjusted model, FBG, SBP, DBP, WBC, and N were signifcantly positively correlated with thyroid nodules compared to their lowest concentration groups, while HDL was signifcantly negatively correlated.Univariate exposure-response functions from BKMR models showed similar results.Moreover, our study found a linear dose-response relationship between the mixture of metabolic and infammatory indicators and thyroid nodules, and SBP was the most important contributor within the mixture.Nevertheless, no interaction efects were found among the fve indicators.Tese associations were more prominent in males, participants with higher age (≥40 years old), and individuals with abnormal BMI.To our knowledge, this is the frst study to examine associations of combined exposure to metabolic and infammatory indicators with thyroid nodules.

Metabolic and Infammatory Indicators.
Consistent with the results of most previous studies [4,33], our study also showed that blood pressure was positively associated with

variable
Figure 3: Single-exposure efects (95% confdence intervals), defned as the changes in the risk of thyroid nodules associated with a change in a particular indicator from its 25th to its 75th percentile, where all of the remaining indicators were fxed at a specifc quantile (the 25th, 50th, or 75th percentile).Adjusted variables included age, gender, diabetes, and hypertension.6 International Journal of Endocrinology thyroid nodules.A recent meta-analysis [33] showed that abnormal blood pressure was associated with thyroid nodules (OR � 1.68, 95% CI: 1.62-1.75).Several large-scale studies, such as [36,37], have concluded that abnormal blood pressure is a risk factor for thyroid nodules.Other studies [26,38] with small samples have yielded inconsistent results.Te exact mechanism of the risk of thyroid nodules due to hypertension is not known.Several studies have shown a positive correlation between TSH and SBP/DBP [39,40], and high TSH levels in hypertensive patients may contribute to the formation of thyroid nodules.In addition, we cannot ignore the possibility of the potential confounding efect of TSH on the association between blood pressure and thyroid nodules.Unfortunately, only 30% of the individuals in this study underwent TSH measurement.Tis severe selection bias hindered the possibility of meaningful mediation analyses, and thus, further studies are warranted to clarify the underlying biological mechanisms.
Consistent with most previous studies [33,[41][42][43][44], we found a signifcant positive association between blood glucose and thyroid nodules.Recently, a meta-analysis [33] also showed that hyperglycemia was associated with thyroid nodules (OR � 1.59, 95% CI: 1.46-1.74).One possible explanation is the confounding efects of insulin resistance.On the one hand, some studies have shown that insulin resistance can promote the formation and growth of thyroid nodules [33,45]; on the other hand, insulin resistance is a key factor in the pathogenesis of impaired glucose metabolism [46].Regrettably, no data on insulin resistance were collected in this study, so the confounding efects of insulin resistance could not be ruled out.As we know, high levels of TSH can lead to the development of thyroid nodules [47,48].A study [41] has shown higher serum TSH levels in serum type 2 diabetic patients than in control prediabetic and control patients, providing another possible explanation.International Journal of Endocrinology In this study, higher levels of HDL were signifcantly negatively associated with thyroid nodules, which was consistent with the limited studies [4,26].A cross-sectional study showed that elevated HDL levels were negatively correlated with thyroid nodules, while TG and LDL were positively correlated [4].Another case-control study also showed a signifcant association between low HDL (OR � 2.77, 95% CI: 1.44-5.30)and thyroid nodules.Compared to the previous two studies [4,26], we used a retrospective nested case-control to provide relatively reliable evidence.However, the underlying mechanism by which high levels of serum HDL reduce the development of thyroid nodules remains unclear.Further studies are needed to examine the prospective association of HDL with thyroid nodules and to better understand the mechanisms.
Our retrospective nested case-control study showed that WBC and N increased the risk of thyroid nodules.Li et al. discovered a higher prevalence of thyroid nodules in participants with high levels of infammation (WBC, N, L, and M) by using propensity score matching for metabolic parameters and other confounding factors [8].Moreover, a retrospective cohort study (included 6587 participants) showed that M was a risk factor for thyroid nodules [48].Haider et al. also reported that the monocyte-to-high-density lipoprotein cholesterol ratio (MHR) and neutrophil-to-lymphocyte ratio (NLR) were signifcantly associated with the presence of thyroid nodules [49].Tese fndings are consistent with our expectations.As we know, chronic infammation plays a role in the development of thyroid nodules [50].

Combined Exposure.
Considering the high correlation and complexity between indicators, traditional methods may not provide a true view of the relationship between mixed exposures to multiple indicators and thyroid nodules.However, to our knowledge, no epidemiological studies are addressing this issue.In this study, we used the BKMR model to assess associations of combined exposure to multiple indicators with thyroid nodules.First, consistent with our expectations, we found a linear dose-response relationship between the combined fve metabolic/infammatory indicators (HDL, FBG, SBP, WBC, and N) and the risk of thyroid nodules.Although both metabolic and infammatory indicators are thought to have an impact on thyroid nodules, their relative importance remains unclear.In this study, we found that metabolic indicators were more important than infammatory indicators.Within this mixture, blood pressure is the most important component.Our fndings suggest that controlling metabolic indicators, especially blood pressure, may be important in reducing the risk of thyroid nodules in adults.

Subgroup Analysis.
Identifying susceptible populations is important for both public health and clinical practice; however, knowledge in this area remains unclear.Most studies have shown a higher prevalence of thyroid nodules in females than in males [4,22,51], but few studies have explored the gender-specifc associations between metabolic/ infammatory indicators and thyroid nodules [22].We found that the association of joint exposure was more prominent in males.In addition, we also found that blood pressure was the main determinant in males and blood glucose in females.Ding et al. found that diabetes (OR � 1.47, 95% CI: 1.17-1.84)remained strongly and independently associated with a higher risk of thyroid nodules in females but not in males.In a retrospective cohort study, Huang et al. found that the association between the metabolic indicator (uric acid) and thyroid nodules was more pronounced in females [48].Although limited results of the gender-specifc association between metabolic indicators and thyroid nodules are controversial, all these results supported the fact that gender may play a moderating role in this association.It is still too early to draw any conclusions on the gender diference in the relationship between metabolic indicators and thyroid nodules, and further studies are warranted.Interestingly, we also found that associations of joint exposure were more prominent in participants with higher age (≥40 years old) and individuals with abnormal body mass index status, which suggested potential harmful efects of high age, under-or overweight status.

4.5.
Implications for Public Health.Our fndings may have implications for public health.Te linear dose-response relationship of the mixture of metabolic and infammatory indicators with thyroid nodules provides valuable insights.Notably, our fndings highlight the dominant role of SBP in the mixture.Tis provides key clues for customizing prevention strategies, suggesting that focusing on managing SBP and considering a combination of approaches (e.g., appropriate medication use and exercise interventions) to intervene with metabolic and infammatory factors.Importantly, our study reveals population-specifc patterns in the observed associations.Males, individuals aged 40 years and older, and those with abnormal levels of BMI showed stronger associations, providing targeted information to optimize prevention strategies.Tis detailed understanding allows for the development of more targeted interventions for diferent populations.For example, for males and individuals with higher age, we can emphasize the critical role of SBP and encourage regular blood pressure monitoring and active blood pressure management.Meanwhile, for individuals with abnormal BMI, we recommend weight management and nutritional education programs to help keep their metabolism and infammation in balance.Tese specifc measures are expected to increase public awareness of health and motivate more people to adopt active lifestyles, thereby reducing the risk of thyroid nodules.

Limitations and Strengths.
Tere are three strengths of this study.First, to our knowledge, this is the frst study to examine the associations of combined exposure to metabolic/infammatory indicators with thyroid nodules.Second, utilizing nested case-control studies helps mitigate selection bias, recall bias, and confounding bias, thereby enhancing the internal validity of associations.Tird, we performed a series of subgroup and sensitivity analyses to show that the results were considerably robust.

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International Journal of Endocrinology However, this study also has several limitations.First, the study population was from one health examination center, which led to possible limitations in the generalization of our fndings to other regions or the general population.Future studies could include data from multiple medical centers or diferent population characteristics to further validate our results.Second, it is important to note that because of the observational research design, we can only infer correlation, not causation.Tird, one-time sample measures may bias internal exposure estimates.We explored the association between baseline and follow-up indicators and found moderate to strong reproducibility for these indicators (ICCs ranged from 0.520 to 0.839).Tus, we believe that one-time sample measures may refect the long-term exposure levels to a certain extent.Fourth, we followed up for only one year, which was unlikely to afect our overall conclusions but limited our ability to assess diferent thyroid grades.For example, the median maximum diameter of thyroid nodules in this study was only 0.30 (interquartile range: 0.24-0.40)mm.Fifth, general several infammatory indicators (e.g., Creactive protein and interleukin-6) were not measured.Terefore, these clinical indicators could not be considered in this analysis, which may underestimate the association of combined exposure to infammatory indicators with thyroid nodules.Finally, since our data came from the health examination center, some confounding factors were not collected well.For example, there was a large amount of missing smoking and drinking, and we could only adjust for these factors in the sensitivity analysis.In addition, residual confounding of unmeasured variables (e.g., physical activities, dietary structure, and iodine content) cannot be excluded.

Conclusions
Our study found a linear dose-response relationship between the mixture of metabolic/infammatory indicators and thyroid nodules, and SBP was the most important contributor within the mixture.Tese associations were more prominent in males, participants with higher age (≥40 years old), and individuals with abnormal BMI.Our fndings suggest that reduced metabolic and infammatory levels, especially reduced blood pressure levels, may be important in preventing thyroid nodules.Further studies are needed to explore the prospective association between metabolic/infammatory indicators and thyroid nodules and to elucidate the complex mechanisms between these indicators and thyroid nodules.with lower age (≥40 years old), estimated using Bayesian kernel machine regression (BKMR).Adjusted variables included gender, diabetes, and hypertension.Supplementary 12. Figure S9: associations between fve metabolic/infammatory indicators and the risk of thyroid nodules in participants with normal body mass index status (18.5-23.9kg/m 2 ), estimated using Bayesian kernel machine regression (BKMR).Adjusted variables included gender, age, diabetes, and hypertension.Supplementary 13. Figure S10: associations between fve metabolic/infammatory indicators and the risk of thyroid nodules in participants with abnormal body mass index status (<18.5 or >23.9 kg/m 2 ), estimated using Bayesian kernel machine regression (BKMR).Adjusted variables included gender, age, diabetes, and hypertension.Supplementary 14. Figure S11: associations between fve metabolic/infammatory indicators and the risk of thyroid nodules in adults, estimated using Bayesian kernel machine regression (BKMR).Adjusted variables included gender, age, diabetes, hypertension, and BMI (continuous).Supplementary 15. Figure S12: associations between fve metabolic/infammatory indicators and the risk of thyroid nodules in adults, estimated using Bayesian kernel machine regression (BKMR).Adjusted variables included gender, age, diabetes, hypertension, smoking, and drinking.Supplementary 16. Figure S13: associations between 14 metabolic/infammatory indicators and the risk of thyroid nodules in adults, estimated using Bayesian kernel machine regression (BKMR).Adjusted variables included gender, age, diabetes, and hypertension.(Supplementary Materials)

Figure 1 :Figure 2 :
Figure 1: Univariate exposure-response functions and 95% confdence intervals for associations between single metabolic/infammatory indicators and the risk of thyroid nodules when other indicators were fxed at the median.Adjusted variables included age, gender, diabetes, and hypertension.

Figure 4 :
Figure 4: Bivariate exposure response functions.Each cell represented the exposure-response curve for the column indicator when the row indicator was fxed at 10th, 50th, and 90th percentiles and the remaining indicators were fxed at their medians.

Table 1 :
Characteristics and metabolic/infammatory indicators of the study population.Due to the missing covariate data, subgroup totals may not sum to the total sample population.b Te chi-square values were calculated without including missing values.c Diabetes was identifed by a fasting blood glucose level of ≥7.0 mmol/L (126 mg/dL).d Hypertension was identifed by a SBP of ≥140 mmHg or a DBP of ≥90 mmHg.Bold values indicate statistical signifcance, P < 0.05.

Table 2 :
Associations between metabolic/infammatory indicators and thyroid nodules using conditional logistic regression.

Table 3 :
PIPs for group inclusion and conditional inclusion into thyroid nodules using Bayesian kernel machine regression (BKMR) models.